Another well known type of deep learning models are Convolutions Neural Networks (CNNs). CNNs are considered to be the state of the art when it comes to speech or image recognition. CNN based models are well known for their ability to capture spatial relationships, nearby elements, e.g., pixels in images, share local features by traversing the input multiple times and applying different filters. CNNs often work on grid-based inputs, limiting its applicability to graph-based inputs such as road networks. Recently, Bruna et al. [1]} introduce Graph Convolutional Networks (GCNs) that combines spectral graphs theory with CNNs. Two recent studies employ GCNs to fill in missing values for uncertain traffic time series [2]}, [3]}.
[4]} proposes Diffusion Convolution Neural Networks (DCNNs) which fall under the non-spectral methods. DCNN works on the assumption that the further two nodes are in terms of graph topology, the lower impact they should have.
